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Jessica Patel — Wed, 28 Jan 2026 03:55:24 +0000

Designing Cache Invalidation at Scale with Spring Boot, Redis, and AWS ElastiCache

Jessica Patel ・ Jan 28

#springboot #redis #awselasticache #java

Designing Cache Invalidation at Scale with Spring Boot, Redis, and AWS ElastiCache

Jessica Patel — Wed, 28 Jan 2026 03:02:39 +0000

TL;DR

How to design cache invalidation for multi‑region Spring Boot systems using Redis and AWS ElastiCache.
How to protect against cache stampedes with single‑flight, stale‑while‑revalidate, and probabilistic expiration.
How to use Redis Pub/Sub as a global invalidation bus and wire it into Spring Boot.

Who this is for: This article is for backend engineers running Spring Boot in production (often on AWS with Redis/ElastiCache) who are hitting scaling or consistency issues with naive caching.

Why Cache Invalidation Gets Hard at Scale

Cache invalidation is famously “one of the two hard things in computer science.” In a single‑node Spring Boot application, it is often treated as a solved problem: add @Cacheable, configure Redis, and move on. At scale, especially in multi‑region, high‑traffic systems, this approach breaks down quickly.

Caching improves latency and reduces database load, but it also introduces state duplication. Once data exists in multiple places—local memory, Redis, and multiple regions—keeping it consistent becomes non‑trivial.

Common failure modes include:

Stale reads after writes in another region
Cache stampedes overwhelming the database
Silent cache divergence between regions
“Fixes” involving global cache flushes that cause outages

The goal is not perfect consistency, but controlled, observable, and bounded inconsistency.

Reference Architecture: Multi-Region Spring Boot Caching

A typical large‑scale deployment looks like this:

Clients routed to the nearest region
Spring Boot services deployed per region
Each region has: local in‑memory cache (for example, Caffeine) and a regional Redis cluster (AWS ElastiCache)
A shared primary database (or active‑active replicas)

This creates three cache layers:

JVM‑local cache (fastest, most fragile)
Regional Redis cache
Source‑of‑truth database

Spring’s cache abstraction is unaware of regions, replication lag, or distributed invalidation. That logic must be designed explicitly.

Cache Invalidation Strategies That Actually Work

Before implementation, it is critical to choose the right strategy.

Cache-Aside (Recommended)

Application reads from cache
On miss, loads from DB and populates cache
On write, updates DB first, then invalidates cache

This provides clear control over invalidation and failure handling.

TTL-Based Expiration (Necessary but Insufficient)
TTL limits staleness but:

Does not prevent serving stale data immediately after writes
Can cause synchronized expirations (stampedes) under load

TTL must be combined with explicit invalidation. If you rely only on short TTLs for correctness, you are already at risk of cache stampedes.

Versioned Keys
Appending a version to cache keys allows mass invalidation without deletes. This works well for schema changes, but less so for fine‑grained updates.

Understanding Spring Boot Cache Internals

Spring Cache provides:

Method interception
Key generation
Cache abstraction over multiple providers

What it does not provide:

Cross‑instance invalidation
Distributed locking
Cache coherency across regions

Annotations like @CacheEvict only evict locally configured caches, not remote JVMs or regions.

Designing Cache Keys for Global Safety

Cache keys must be:

Deterministic
Namespaced
Versioned

A robust key structure looks like:
{service}:{entity}:{tenant}:{id}:v{schemaVersion}

Key versioning allows zero‑downtime changes and prevents collisions during deployments.

Multi-Region Consistency Models

Strong consistency across regions requires synchronous coordination, which increases latency and reduces availability. Most systems choose eventual consistency with guardrails:

Writes invalidate caches asynchronously
Reads may see stale data briefly
Business logic defines acceptable staleness windows

Trying to enforce global “read‑your‑writes” usually causes more harm than benefit for typical web workloads.

Cache Stampede: The Hidden Scaling Killer

A cache stampede occurs when:

A popular key expires or is invalidated
Thousands of concurrent requests miss the cache
All requests hit the database simultaneously

This can cascade into:

Database overload
Thread pool exhaustion
Region‑wide outages

TTL alone makes this problem worse by synchronizing expirations. For example, a 10k QPS endpoint with a 60‑second TTL can easily send thousands of requests to the database in a single second if a hot key expires everywhere at once.

Stampede Protection Techniques

These patterns are most useful on read‑heavy, hot paths. For low‑traffic or write‑heavy entities, they may be unnecessary complexity.

Probabilistic Early Expiration
Instead of expiring keys at a fixed time:

Add jitter to TTLs
Allow early refresh based on probability

This spreads refresh load over time. Avoid using complex probability logic on very low‑traffic keys; the added code rarely pays off there.

Request Coalescing (Single-Flight)
Only one request per key should rebuild the cache:

Use a Redis‑based lock per cache key
One instance becomes the “leader”
Others wait briefly or serve stale data

Locks must:

Have timeouts
Be fail‑safe
Never block indefinitely

This is powerful on extremely hot keys, but do not overuse per‑key locks on cold data—it adds operational complexity for little benefit.

Stale-While-Revalidate
Serve stale data while refreshing in the background:

Improves availability
Prevents user‑facing latency spikes
Requires explicit correctness checks

This pattern is extremely effective in read‑heavy systems where slightly stale data is acceptable, but it is a poor fit for financial or strongly consistent domains.

A Single-Flight Cache Load (Pseudo-code)

Below is pseudo‑code for a single‑flight cache load flow; treat it as a sketch, not drop‑in production code.

// Single-flight cache load for key (pseudo-code)
CacheValue getOrLoad(String key) {

    if (localCache.contains(key)) {
        return localCache.get(key);
    }

    // Try acquiring Redis-based lock for this key
    boolean lockAcquired = redisLock.tryLock(key, 5, TimeUnit.SECONDS);

    if (lockAcquired) {
        try {
            CacheValue value = db.load(key);      // Load from DB
            localCache.put(key, value);           // Populate local cache
            redisCache.put(key, value);           // Populate Redis
            return value;
        } finally {
            redisLock.unlock(key);
        }
    } else {
        // Fallback: serve stale cache if available, else wait briefly or fail
        return localCache.getOrDefault(key, fetchStaleOrFail(key));
    }
}

Failures should degrade gracefully to DB reads, with clear metrics so you can see when you are falling back too often.

Redis as a Global Invalidation Bus

Polling for invalidation does not scale. Instead, use event‑driven invalidation.

Redis Pub/Sub is well‑suited for this purpose:

Low latency
Simple semantics
Native support in ElastiCache

However, Redis Pub/Sub:

Does not guarantee delivery
Does not persist messages

This is acceptable for invalidation if:

TTLs exist as a safety net
Invalidation messages are idempotent

For stricter guarantees or auditability, you may need something like Redis Streams or Kafka instead of bare Pub/Sub.

Implementing Pub/Sub Invalidation with AWS ElastiCache

Channel Design
Use namespaced channels such as: cache-invalidation:{service}

Message Payload
Messages should be small and structured:

Cache key or key pattern
Entity type
Version
Timestamp Never include sensitive data.

ElastiCache Considerations

Pub/Sub works across nodes within a cluster
Cross‑region invalidation requires application‑level forwarding, or regional producers publishing to all regions
Avoid synchronous cross‑region calls on the write path

These considerations keep your writes fast while still achieving eventual consistency across regions.

Wiring Pub/Sub into Spring Boot

In practice you will define a CacheManager (for example, using Caffeine + Redis) and a dedicated RedisTemplate bean. The code below is illustrative and focuses on the invalidation flow rather than exact configuration.

@Service
@RequiredArgsConstructor
public class UserService {

    private final UserRepository userRepository;
    private final CacheManager cacheManager;
    private final RedisTemplate<String, String> redisTemplate;

    // Read-heavy operation, cache in both local and Redis layers
    @Cacheable(value = "users", key = "#id")
    public User getUser(Long id) {
        return userRepository.findById(id)
            .orElseThrow(() -> new EntityNotFoundException("User not found"));
    }

    // Write operation triggers explicit cache eviction
    @Transactional
    @CacheEvict(value = "users", key = "#user.id")
    public User updateUser(User user) {
        User updated = userRepository.save(user);

        // Publish invalidation message to Redis for multi-region propagation
        String channel = "cache-invalidation:users";
        String message = user.getId().toString();

        redisTemplate.convertAndSend(channel, message);

        return updated;
    }
}

@Component
@RequiredArgsConstructor
public class RedisCacheInvalidationListener {

    private final CacheManager cacheManager;

    // Illustrative annotation – configure according to your Redis listener setup
    @RedisListener(topic = "cache-invalidation:users")
    public void onMessage(String userId) {
        // Evict local caches (Caffeine or similar)
        cacheManager.getCache("users").evict(Long.valueOf(userId));

        // Optional: evict other Redis caches if needed
    }
}

Local caches (for example, Caffeine) must be explicitly cleared—Redis invalidation alone is insufficient.

End-to-End Invalidation Flow

Write Path

API updates database → transaction commits(t0)
Application evicts regional Redis cache immediately (t0+δ1)
Publish invalidation message to Redis Pub/Sub (t0+δ2)
All instances receive message and evict local caches asynchronously (t0+δ3 → t0+δ4)

Notes:

δ1 – δ4 represent small asynchronous delays; reads may see stale data briefly
Guarantees eventual consistency, not immediate consistency across regions

Read Path

Check local cache first
Check Redis cache if local miss
On miss: acquire single‑flight lock → load from DB → populate caches → release lock

Failures at any step degrade gracefully to DB reads.

Observability: Knowing When Caching Is Failing

Without observability, cache bugs remain invisible. Key metrics:

Cache hit ratio (local vs Redis)
Stampede lock contention rate
Invalidation propagation latency
DB fallback rate Logs should include:
Cache key
Region
Correlation ID

Distributed tracing can show sequences like “cache miss → DB spike → invalidation lag,” which helps you debug issues quickly.

Performance and Cost Trade-Offs

Caching is not free. Costs include:

Redis memory
Network traffic from Pub/Sub
Increased application complexity

Trade‑offs:

More aggressive caching reduces DB cost
Over‑caching increases Redis cost and invalidation load Optimize based on measured behavior, not assumptions.

Security and Safety Considerations

Protect your invalidation mechanism:

Restrict Redis access via security groups
Validate message payloads
Guard against wildcard evictions
Add feature flags to disable invalidation logic during incidents One malformed invalidation message can flush an entire region.

Common Anti-Patterns

These patterns create fragility rather than correctness:

Global cache flushes in production
Short TTLs used as a consistency crutch
Synchronous invalidation across regions
Assuming Redis Pub/Sub is reliable messaging

Naive Caching vs Designed Invalidation

Dimension	Naive caching at scale	Designed invalidation
Correctness	High risk of stale reads	Event-driven, eventual consistency enforced
Blast radius	Global cache flushes can wipe all regions	Targeted key eviction; limits impact
Operational risk	High: outages and DB overload possible	Controlled, observable, safe recovery
Cost	Low caching ops but high DB/incident cost	Slightly higher caching/invalidation ops, lower DB load
Complexity	Low to implement	Medium: needs locks, Pub/Sub, and monitoring

Designing invalidation intentionally turns caching from a constant source of outages into a predictable, observable subsystem.

Extending the Architecture

For stricter guarantees:

Combine Pub/Sub with versioned keys
Use Redis Streams or Kafka for durable invalidation
Add read fencing for critical entities

As systems evolve toward active‑active databases, invalidation becomes a first‑class architectural concern.

Conclusion

Cache invalidation at scale is not an annotation problem—it is a distributed systems problem. In multi‑region Spring Boot deployments, correctness emerges from explicit invalidation design, stampede protection, event‑driven coordination, and strong observability.

AWS ElastiCache and Redis Pub/Sub provide powerful building blocks, but only when used deliberately. A well‑designed cache invalidation strategy prevents outages, reduces costs, and enables systems to scale safely.

AI-Powered Testing: Writing Smarter, Faster Unit Tests with GitHub Copilot

Jessica Patel — Wed, 31 Dec 2025 18:57:47 +0000

As developers, we understand how automated unit testing is essential in modern software development. It helps detect bugs early, improve code quality, minimize human error, streamline refactoring, accelerate development cycles, and ultimately lower costs. Most importantly, it ensures the software behaves as intended and meets its specified requirements.

Nevertheless, its traditional approach can feel like an uphill battle–often repetitive, time-consuming, and requiring deep knowledge of application logic and testing frameworks.

This is where GitHub Copilot comes into play. Even though it was initially intended for code generation, Copilot also excels at automating test creation by providing not just one or two but multiple suggestions that you can cycle through to enhance accuracy and efficiency.

In this article, we’ll walk you through how you can use GitHub Copilot to streamline unit testing, improve test coverage, and save valuable development time.

Setting Up GitHub Copilot for Testing

Before diving in, let’s first discuss unit testing in software. However, if you are already familiar with this, feel free to skip to the next section.

What is Unit Testing?

Like any other product or service, a software application must be thoroughly tested before it’s made available on the market. It’s a crucial phase in the Software Development lifecycle (SDLC) that usually involves assessing the functionality of a program to ensure it checks all the boxes.

When it comes to unit testing, this procedure trickles down to inspecting the smallest functional units (functions, methods, and classes) to certify they operate accordingly in isolation. This is meant to find bugs and ascertain all components run smoothly and accurately before launching.

Nonetheless, manual unit testing is not a walk in the park. Often because it’s a time-consuming, monotonous, and error-prone operation. What’s more, it requires dedication and attention to detail.

Sometimes you might even write unnecessary tests with far less value than expected. Hence the need to streamline the process.

Installing and Configuring Copilot in VS Code

Before installing GitHub Copilot in VS Code you need to meet the following requirements:

Prerequisites

GitHub Account — register here if you don’t have one.
Supported Programming Language SDK — install the SDK for your go-to language like Python, JavaScript, TypeScript, etc.
Preferred IDE — in this case VS Code.

Setting Up VS Code Copilot

Start by installing Copilot extensions in your VS Code using the link below: Install the GitHub Copilot Extension here or search for the GitHub Copilot from the extension view as illustrated. Restart VS Code after installation.

Sign in to your GitHub account using the Chat View on the right panel. To start using Copilot, ensure the extension is active–do this by clicking on the Copilot icon at the bottom right of your VS Code.

If you start seeing suggestions as you type, the installation was successful.

There are several ways to interact with Copilot, the most obvious and by far the easiest one being Copilot Chat. Here, you interact with Copilot in an interactive, chat-like manner by sending prompts in the form of text. If you’ve ever used Copilot in your GitHub account or ChatGPT, this should be like riding a bike.

There is also an inline code suggestion which offers real-time suggestions (appear as grayed-out text) as you type.

And for those who are old-fashioned like me, simply select the code you want to test, right-click, and select Generate tests. Additionally, you can use the slash command /tests in your Copilot Chat or IDE to generate tests.

This is just the tip of the iceberg. There are many ways to interact with Copilot, and the more you use GitHub Copilot, the more you’ll discover them.

How Copilot helps in writing Unit Tests

In your first encounter with Copilot, you might think it’s an autocomplete feature on steroids–except its suggestions are brilliant and can expand entire lines or functions. It uses an AI language model (Open AI GPT-3) trained on GitHub’s dataset to draw context from your code or chats and provide relevant code suggestions, including unit tests.

How does this work practically?

Imagine testing a login form manually. It seems straightforward enough till the application scales and you are required to repeat all test cases (valid login, invalid login, empty fields, etc.).

To avoid this hassle, you can use Copilot to streamline the process on your behalf. And there is no shortage of options when it comes to automating unit test generation.

Suggesting Test Cases based on Function Signatures and Comments

Start by providing functions and comments with well-defined purpose and functionality. Copilot will automatically pick this up and generate meaningful test cases without additional input.

Sample Comment: “# This function takes two integers as input and returns their sum.

# It is a simple arithmetic operation commonly used in mathematical calculations.”

From the above comments, Copilot will suggest a function that adds two numbers as shown below:

To accept, click ‘tab’ or use alt + [ and alt + ] to cycle through the options.

GitHub Copilot will provide unit tests for the function generated above and proceed to test them.

Note: since it’s generative AI, your output might differ from the above.

Generating Unit Tests for Different Frameworks

Copilot supports various languages and testing frameworks but works particularly well with Python, JavaScript, TypeScript, Ruby, Go, C# and C++.

Below are code snippets of GitHub Copilot in action, generating test cases for different frameworks.

For the Python function below:

Copilot generates the following PyTest test case:

Similarly, for Jest in JavaScript:

As you can see, it is easy to write unit tests for various frameworks without knowing the language deeply. This enables developers to focus more on functionality and logic, and the testing process becomes easier and faster overall.

Completing Assertions Intelligently

Copilot can automatically fill in assertions from a function output. If your function must return a list, dictionary, or specific value, Copilot will often supply appropriate assertions.

For instance, a function that returns a user dictionary:

Copilot suggests:

Mocking Dependencies and API Calls

In situations where you have functions that rely on external databases or APIs, Copilot will assist you in generating mock objects and responses.

Example: Mocking an API Call with unittest.mock in Python

Copilot generates:

Best Practices for Generating Effective Tests with Copilot

Even though GitHub is an impressive tool that significantly speeds up the process of writing tests, you still need some tips and tricks to get the best out of it. Here are some of them:

1. Providing clear function docstrings for better test suggestion

When using Copilot, keep in mind that it doesn’t code exactly like you do. So for you to consistently generate high-quality test cases, ensure your functions are well documented to guide Copilot effectively.

Example :

2. Be specific in your prompts about what you want to test

Don’t entirely rely on Copilot’s default suggestions. Instead, provide specific prompts and, if need be throw in some examples to reduce chances of misinterpretation that might lead to the generation of basic cases while overlooking edge cases.

Example of a weak test prompt :

“Write a test for calculate_discount”

Example of a strong test prompt:

“Write unit tests for calculate_discount. Include cases for 0% discount, 100% discount, a typical discount (e.g., 20%), and invalid discounts (-10%, 110%). Use pytest and check for expected exceptions”

The above prompt yields an all-rounded set of test cases unlike the former.

3. Reviewing and refining generated tests

Do you review your code? The same principle applies here. When coding with GitHub Copilot, remember that it generates code by finding patterns and learning from existing public code.

Although it may accelerate development by providing good suggestions, it does not understand the context of your specific project or business rules. Even with good docstrings and solid prompts, there’s a need for human inspection to ensure logical correctness, handling edge cases, security, performance optimization, and maintainability.

4. Combining Copilot with manual testing strategies

Every time Copilot generates unit tests, you might be tempted to sit back and accept every suggestion it throws at you. Don’t! Not only will you end up being over-reliant on Copilot, but you might also end up with generic test cases. To avoid this, complement Copilot-generated test cases with manually written cases.

Example:

When you start typing a function:

Copilot will automatically suggest:

If you are keen, you’ll notice a few issues in the above code. For instance, there are no tests for empty values, case sensitivity, or incorrect passwords. To improve this we need to manually refine these test cases.

Take a look at the snippet below.

Copilot’s Test Generation in Action: Real-World Scenario

Let’s go ahead and look at a practical example in Python — one of the most widely used languages in the world currently.

Example: Generating unit tests in Python with Copilot

For this example, let’s use a simple Python Function that calculates the final price of a product after applying a discount. Then proceed to generate unit tests using GitHub Copilot.

Enter the following slash command in Copilot Chat. /tests create tests for calculate_discount.

Copilot will generate appropriate unit tests and you’ll see an output similar to the one below. ( bear in mind that this is generative AI and your results might be a little bit different)

From the above code, we can see GitHub Copilot creating unit tests for the calculate_discount function. Let’s break the code to understand what’s happening.

The unit test has two methods to check for valid and invalid discounts. The valid method covers various scenarios; a typical discount (10%), a 50% discount, a 0% discount (no discount), and a 100% discount (free). Here, Copilot uses self.assertAlmostEqual which is appropriate for floating-point calculations with small margins.

The invalid method checks for invalid discounts and raises a ValueError using self.assertRaises(ValueError).

Modifications and Improvements

While these tests are quite good and work as expected, we can still make a few adjustments such as:

Providing more edge cases.

We included a floating-point discount test to ensure proper handling of decimal discounts (e.g., calculate_discount(100, 12.5)), a large price test to ensure the function’s proper handling of large values (e.g., calculate_discount(1,000,000, 50)), and a small price test to ensure it properly handles very low values (e.g., calculate_discount(0.01, 10)). These additions improve the function’s accuracy and robustness.

Improving clarity and readability

We organized the test methods for readability and maintainability. test_valid_discount tests the function’s behavior given valid discount percentages, e.g., the additional cases we included. test_invalid_discount tests that a ValueError is correctly raised for invalid discount percentages, such as negative (calculate_discount(100, -10)) and above 100% (calculate_discount(100, 110)). This organization enhances test coverage and readability.

Testing for correct error messages

We utilized assertAlmostEqual to address floating-point precision differences and assertRaisesRegex for testing correct error messages for incorrect discount percentages and yielding accurate and consistent test output.

Limitations & Challenges

GitHub Copilot has its setbacks. Knowing how to navigate these challenges is crucial as you continue generating unit tests with Copilot.

Common pitfalls of AI-generated tests

One of the biggest challenges developers face with GitHub Copilot is ensuring it accurately interprets chat inputs. Achieving the desired results often requires a series of well-structured prompts. Even then, Copilot may struggle to retain context from previous interactions, particularly in complex testing scenarios.

At times, developers have reported receiving irrelevant or nonsensical code suggestions from Copilot. In some cases, it may even stop generating suggestions altogether, which can be frustrating — especially when working mid-task.

Like other large language model (LLM)-based tools, GitHub Copilot raises ethical concerns, primarily because it is trained on publicly available GitHub repositories. As a result, it may occasionally generate code that includes sensitive information, such as API keys, usernames, or passwords, if such data was present in the training set.

Always review Copilot-generated code for security flaws, such as hardcoded credentials or insufficient validation as it could inadvertently introduce or amplify existing bugs and vulnerabilities, potentially exposing systems to security threats that malicious actors might exploit.

When is Human intervention necessary?

When using Copilot to generate tests, keep in mind that it is simply an AI-powered coding assistant. It generates code by recognizing patterns from its training data, but it lacks true understanding of code semantics and cannot assess the quality of its output. In other words, if Copilot is trained on flawed data, its suggestions may also be flawed.

This is where human oversight becomes essential. Unlike Copilot, you have the ability to step back, evaluate the context, and make informed decisions. Your creativity and adaptability — skills that AI currently lacks — are crucial in ensuring the accuracy, efficiency, and reliability of the generated tests.

Copilot’s effectiveness across different languages & frameworks

Copilot is designed to support a wide range of programming languages and frameworks, but its effectiveness varies. This is largely due to its reliance on publicly available GitHub data for training.

The tool performs best with widely adopted languages such as Python, JavaScript, Java, and C#, where extensive training data is available. It delivers moderate results for languages like Ruby, Swift, Kotlin, and Go.

However, for less common languages like Rust, Haskell, and Perl, Copilot’s effectiveness diminishes, as there is less training data to draw from. The same limitations extend to their associated frameworks.

Conclusion

While code completion tools have been around for a while, GitHub Copilot elevates the experience to an entirely new level. By streamlining unit test creation, it transforms what was once a tedious, manual process into a faster, more interactive, and intelligent workflow.

However, Copilot isn’t a substitute for manual testing. Understanding when and how to use it is crucial. AI-generated tests can sometimes miss critical scenarios, overlook edge cases, or misinterpret complex logic. The key to writing effective unit tests lies in striking the right balance between Copilot’s automation and human oversight.

If writing manual tests feels like a challenge, Copilot can be a game-changer. But don’t just take our word for it — give the free version a try, explore its capabilities, and share your thoughts in the comments.

Vibe Coding Explained: How AI Is Changing the Way We Build Software

Jessica Patel — Tue, 23 Dec 2025 20:37:10 +0000

AI is redefining how software is built. Today, you no longer have to write lines of code to develop an app, system, network, or anything for that matter. With AI coding tools like Lovable, Replit, and Cursor, anyone can build functional applications by simply describing what they want to achieve. This is what “Vibe Coding” or “programming by vibes” is all about.

For old-school programmers who have been burning the midnight oil over the years, mastering how to design, code, debug, and test software, the idea that you can simply blink software into reality with a few prompts is far-fetched. Nonetheless, Vibe Coding is real.

In this article, we’ll explore how this trend is bridging the gap between technical and non-technical individuals, developers, and users, a feat that was previously impossible. Let’s break it down.

What Is Vibe Coding?

Unlike other trends, which often leave you wondering how and when they started, this can be traced back to March 2025, when Andrej Karpathy tweeted, “There’s a new kind of coding I call Vibe Coding, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists”.

Simply put, vibe coding is a technique where you lean on AI agents to write most of the code for the apps you want to build by using descriptive prompts or “vibes.” In other words, it has less to do with knowing how to write every line of code and more to do with knowing what you want to build and how to describe it clearly.

Your role as the engineer shifts to interacting with the tool, examining its output, and steering it in the right direction. Even when errors occur, the expectation is that the agent will iterate, debug, and fix its own mistakes.

The upside of vibe coding

Rapid prototyping: With less code to write by hand, vibe coding accelerates the time it takes to turn an idea into a working project, making it easier to spin up early versions of applications.
Streamlines routine tasks: All the “boring” parts of engineering, such as setting up environments and scaffolding, are handled in minutes instead of hours, freeing developers to focus on high‑level design instead of boilerplate.
Boosts creativity: By lowering the cost of experimentation, vibe coding encourages a more fluid, intuitive approach to building and iterating on ideas.

Practical applications of vibe coding

Regardless of whether “vibe coding” becomes the official name, the trend itself is here to stay, and it is already gaining traction in several areas:

Startups

Vibe coding has proven to be a game‑changer for startups looking to move fast with limited resources. It enables a handful of people, or even solo founders, to rapidly create prototypes and MVPs, bringing ideas to market quickly and efficiently.

Agile teams

Initially, the adoption of AI by agile teams was gradual. Over time, though, we have seen a complete shift, with AI not only assisting with agile development but becoming the backbone of how many high‑performing teams operate. By automating repetitive tasks, vibe coding frees developers to focus on problem‑solving and innovation.

Enterprises

Maintaining and updating legacy systems in a large organization is no small feat, especially when done traditionally. The process can take weeks or even months. With vibe coding, enterprises can modernize their systems more easily as AI assists with code refactoring, identifies outdated patterns, and suggests more efficient solutions.

Open source

In the open‑source world, vibe coding streamlines the contribution process for developers of all skill levels. It helps contributors get up to speed quickly by suggesting relevant code, surfacing documentation, and checking for common errors.

From these examples, you can get a glimpse of how different organizations are already using vibe coding. But to understand its power, you have to see it in practice. It is one thing to talk about automating tasks and increasing efficiency; it is another to watch it unfold in front of you. So let’s move from the conceptual to the tangible.

Vibe coding in action

The first time vibe coding really “clicked” for me was when I decided to build a small AI‑powered task manager from scratch, without touching a code editor. Instead of opening an IDE, I opened an AI coding assistant, pasted a single sentence — “Create a simple AI task manager app with JavaScript and HTML” — and waited to see how far the machine could take me.

For that experiment, I used Lovable, but the same approach works with tools like Cursor, Replit, or Windsurf, and with general‑purpose LLMs such as ChatGPT or Gemini.

Within minutes, Lovable scaffolded both the frontend and backend, wired up a basic UI, and even suggested improvements: better validation, nicer styling, and small UX tweaks I had not explicitly asked for. My role shifted from “person who writes every line” to “person who describes the outcome, reviews diffs, and presses the accept button when the vibes feel right.

Linking GitHub turned it into a real workflow instead of a one‑off demo. Each push to the main branch triggered Lovable to re‑ingest the codebase, propose changes, and keep the app deployable, so I could alternate between manual edits and AI‑guided refactors without losing the thread. It felt like pair‑programming with a very fast, slightly overconfident junior developer that never gets tired of trying again.

How it compares with traditional coding methods

Putting vibe coding next to a traditional IDE makes the trade‑offs obvious. On the AI side, the learning curve is shallow: you describe the app in natural language, iterate on prompts, and rely on the model to generate boilerplate, glue code, and even tests far faster than a human could. In a traditional setup, you retain full control over every line, but you also take on all the cognitive load of designing, wiring, and maintaining that code yourself.

Traditional IDEs, on the other hand, give developers full control over the project from the very beginning, which is ideal for complex systems that demand a high degree of precision and customization. This control comes with a steep learning curve, though, because it requires deep knowledge of languages like Python, Java, or C, plus a broad set of engineering skills that can take years to master.

Here’s a quick summary:

Challenges of vibe coding with AI

The first AI‑generated prototype feels like magic; the third or fourth reminds you why software engineering is still a job. The problems only really show up after the “wow” moment, when you try to turn a demo into something robust enough to survive real users.

Three pain points kept repeating in my experiments:

AI agents are great for simple CRUD prototypes, but struggle as soon as you add non‑trivial business logic, tricky state, or performance constraints. At that point you need to guide them with very specific prompts and be ready to step in with manual fixes.
The model will confidently tell you “Done!” even when the feature silently fails. You only discover the lie when you click the button, nothing happens, and you end up debugging AI‑written code you did not fully read.
Progress is front‑loaded: you sprint to 80 percent completion, then crawl through the last 20 percent as edge cases, integration bugs, and weird regressions pile up. Without a basic understanding of software engineering, it is very easy to accumulate a graveyard of almost‑finished apps that never quite ship.

Vibe coding can absolutely multiply the output of someone who already knows how to design, test, and debug systems; it is much less forgiving for people who hope to outsource all of that thinking to the model.

What the community and developers are saying

Ever since Karpathy coined the term, vibe coding has ignited a passionate, often divisive conversation within the developer community. While some are hyping it up, others are approaching it with caution and skepticism, and others are outright rejecting it, calling it a “gimmick.” But behind all the noise, people out here are benefiting and profiting from it.

For example, after realizing that things could move way faster with AI, Rowan Trollope, the CEO of Redis, approved the use of AI‑assisted coding tools. Other companies following the same trend include Reddit, Google, Visa, and Bayer, among others. In fact, according to Y Combinator, 25% of its startups are using AI for 95% of their code base.

And it is not just big companies. Harry Roper, who founded Imaginary Space Agency, created a $100,000‑per‑month business using Lovable to develop production‑ready applications. We also have examples of non‑programmers, such as Kevin Roose, who shared his experience in an article in the New York Times, as shown below.

What lies ahead

What we are currently seeing is just the tip of the iceberg. It is not just about meeting today’s needs, but about building toward a future development experience that feels more like a conversation than a chore. Here is what is on the horizon:

Multi‑language support

Vibe coding will go beyond popular programming languages like Python, JavaScript, and Java. It will increasingly support niche and emerging languages like Go, Julia, and Swift, and make it possible to shift projects between languages with minimal manual rewriting.

Voice‑to‑code

Soon enough, you will be able to dictate features in plain speech and watch your AI agent translate them directly into functional code. Speaking your intent instead of typing it will make prototyping even more accessible.

Collaborative, real‑time AI dev rooms

Vibe coding will enable you and your team to join virtual development rooms where ideas can be shared, built, edited, and deployed in real time. Each teammate will be able to work in parallel with AI assistance, without constantly overwriting each other’s changes.

Final thoughts

To sum it up, vibe coding is a new, more powerful approach to building software, not a replacement for human expertise. It is a force multiplier for developers who have put in the work and built a strong command of their craft.

For up‑and‑coming engineers, it is not a shortcut to avoid learning, but a glimpse of what they can achieve with a solid foundation. The most successful developers of the future will not be the ones who choose between traditional coding and vibe coding, but the ones who blend both to build what was previously impossible.

Do not just take this on faith. Experiment with an AI coding tool today and see how this new approach can accelerate your next project.

Bruno API: How to Get Started with This Lightweight API Testing Tool

Jessica Patel — Wed, 19 Feb 2025 20:34:22 +0000

Managing API collections is an important task for software developers, and this is where API clients come into play. While Postman and Insomnia are prominent tools in this field, their limitations have prompted developers to explore other API clients. As a result, many have adopted Bruno, a newer solution that has gained significant traction in the past year or two.

Not just because it’s new but also because it revolutionizes the status quo presented by Postman, Insomnia, and other similar tools out there. But what is Bruno and how does it stack up against these tools? This is what we’ll be covering in this article. By the end of the article, you’ll have a grasp of:

What Bruno is.
How to install and use it.
How it compares with Postman and Insomnia, and how it edges them out.

Let’s begin.

What is Bruno?

Bruno is a modern open-source API Client with intuitive interfaces used for testing, debugging, and managing requests. It mainly focuses on enhancing the developer experience with its minimalist design and improved overall performance due to its lightweight nature.

Bruno’s popularity among software engineers in API development has been on the rise. One of the major reasons dates back to May 14, 2023, when Postman decided to discontinue the Scratch Pad model which had offline capabilities.

This meant that developers had to log in to access their collections which were hosted in the Cloud after synchronization. The same scenario happened with Insomnia on September 28, 2023, when they released version 8.0. Without logging in, developers had limited functionality.

Therefore they had to search for other API Client tools that isolated API workspace from third-party servers. And that’s where Bruno comes in.

Why choose Bruno?

Being an open-source API Client, it allows developers to use, modify, and distribute it freely. This encourages a communal approach and transparency to its improvement and development.

With a major emphasis on collaboration, data privacy, and security your collections are stored offline which can be shared as a file or be worked on collaboratively via standard version control systems such as Git. Additionally, you are not required to log in to access your data.

Bruno also allows you to import your collections from Postman and Insomnia, and use them in different environments in the same way as other API Clients.

How to set up and use Bruno?

Prerequisites

To follow along with this tutorial, you will need:

To Install Bruno: You can download Bruno from the official website or GitHub repository. It’s available for Windows, macOS, and Linux.
Some APIs for testing: You can get fake APIs from JSONPlaceholder.

Creating a GET Request

Step 1: Creating a Collection

After installing Bruno, open the Bruno app and click on Create Collection:

In the popup, enter your preferred Collection name. Select the location of the directory in your file system then proceed to save your collection.

Step 2: Adding an API Request

On the newly created collection, click on the three dots to display a drop-down menu from which you can select ‘ New Request ’.

Add the name of the request and the request URL as https://jsonplaceholder.typicode.com/posts/1.

Step 3: Incorporating Test Checks

To do this, you can use Bruno’s built-in test-assert option on the panel:

This has three parameters for you to fill in–Expression, value, and, operator.

Example Assertions

Verify Status Code

Expression: res.status

Operator: equals

Value: 200

You can debug your assertions by clicking the run button (shaped like a right-facing arrow) and check if the results are as expected.

Using JavaScript

You can also use JavaScript to debug. Click on ‘ Tests ’ below Demo1 to open a panel then enter the code below.

function testStatusCode(response) {

if (response.status === 200) {

return true;

} else {

return false;

}

You can view the results as expected on the right panel.

A Comparison of API Client Tools: Bruno, Insomnia, and Postman

Each of these API clients has different strengths. knowing which one to select can have a massive impact on productivity and workflow.

Popularity and ecosystem

Of the three, Postman is the most popular and widely used API Client. This is because it has extensive documentation, a large active community, and excels at collaboration, which allows developers to share workflows, environments, and collections.

Insomnia comes in second and is highly valued for its sleek nature, intuitive interface, and focus on simplicity in API testing. However, it has a smaller community and fewer resources compared to Postman.

Bruno on the other hand is still relatively new and has yet to establish a large community and ecosystem but its popularity is steadily rising.

Performance and resource usage

Bruno’s lightweight design makes it accessible even to less powerful devices, making it highly efficient in resource-constrained environments.

Postman, with its extensive options and cloud integration, tends to demand more system resources. This will be disadvantageous for users with older devices.

Insomnia falls somewhere in between, offering a good balance of performance and functionality without being overly resource-intensive.

Automation and testing

Postman excels in this section. With advanced features like Collection Runner and Newman CLI, users can automate API testing and integrate these workflows into CI/CD pipelines.

Insomnia is not as advanced as it offers basic testing capabilities and allows users to extend its functionality through plugins.

Similarly, Bruno lacks advanced features for testing and automation as it’s constrained to basic functionality.

User interface and Usability

Postman has a more detailed and complex interface mainly used for managing large-scale projects. This tends to be intimidating and overwhelming, particularly for beginners.

Insomnia on the other hand has a clean and intuitive interface making it more user-friendly for beginners and developers focusing on simpler workflows.

Bruno uses the minimalistic approach by providing a lightweight and straightforward interface.

Collaboration and Teamwork

Postman has extensively advanced features that are applicable in this area such as shared workspaces, collection sharing, Postman API and mock servers, version control, real-time sync, and notification. These features make Postman stand out when it comes to collaboration and teamwork.

Insomnia also supports teamwork and collaboration though not as elaboratively as Postman. It also requires login and internet connectivity which can be quite limiting.

Bruno has a native Git-centric design allowing it to seamlessly integrate with Git offering a decentralized approach to collaboration that allows developers to share collections and updates effortlessly.

How Bruno outperforms Postman and Insomnia

Now that we’ve looked at how Bruno generally stacks up against these two powerhouses, let’s dive into its unique features. What makes it stand out from the crowd is that it isn’t just a new tool in the market but one that offers a different kind of solution contrary to the existing API Clients.

Lightweight and User-friendly

Bruno’s design is simple and intuitive, focused on the essentials — creating, testing, and managing API requests. This allows users to quickly access necessary features thus working faster. For beginners, it means a reduced learning curve as the UI is clean, distraction-free, and easy to navigate seamlessly from collections, requests, and settings.

This gives it an edge over Postman, where developers must navigate multiple menus to find specific features.

Bruno’s lightweight nature stems from its minimalistic design. This translates to faster load times and smoother performance across various devices including low-spec and older systems.

Similarly, Bruno utilizes significantly lower memory and CPU than Postman making it ideal for developers working with fewer resources.

This allows developers to run Bruno simultaneously with multiple applications such as an IDE, browser, and database tool without experiencing a system lag or decline in performance.

Collections are stored within your repository

Bruno’s developers created the Bru Markup Language specifically to address the way collections are saved and exported.

With Postman and Insomnia, you must export and save collections as a single JSON file. This forces developers to useproprietary version control systems to be able to collaborate.

This is not the case with Bruno. The Bru markup language makes it possible for users to store API requests as plain text. This means your API collections are saved in a folder within your code repository allowing you to choose a version control system of your liking.

With this, collaboration becomes easy by pulling requests as human-readable files which enables developers to understand any changes made to the API collection.

Below is an example of a GET request including some parameters.

How API Requests are made

Making an API request in the Postman Web App involves routing the request via their proprietary proxy server which usually acts as a bridge between Postman and the target API server. To do this, you’ll need to provide the proxy’s server address, port, and authentication credentials in the Postman’s settings.

Reports also suggested that the Postman desktop app has been using a proxy server to make API requests. This poses a huge security risk.

Contrary to Postman, API requests in Bruno are made directly from your computer. Giving you full control of your data.

Here’s an example of how API requests are made in Bruno:

Collection Runs

This is a crucial feature in API Clients as it allows developers to automate the execution of multiple API Requests, saving time and maintaining consistency in testing.

Bruno allows its users to carry out collection runs an infinite number of times free of charge.

Below is an example of how to perform a collection run in Bruno:

Postman Collection Runner is available at 25 runs per month for the free and basic plan, 250 runs per month for the professional plan, and infinite runs per month for the enterprise plan.

This has sparked a lot of debate. For instance, what problem is it solving? Why would there be a limit? With a feature using no Postman Cloud resources when running locally, it makes no sense to pay for it.

NPM modules support

Bruno has numerous Pre-installed Libraries to facilitate your scripting. These include:

However, if you have a particular external library in mind, you can easily install it using package.json. Use this External Libraries documentation to get you started.

Unlike Bruno’s straightforward integration with NPM modules, Postman requires workarounds to achieve the same goal which can complicate workflows. This involves loading from CDN and saving the entire library library in collection variables to load npm modules.

Support for CLI

Bruno allows users to run API collections effortlessly using simple command line commands.

So if you want to test your APIs in different environments, automate your testing process, integrate your API tests, or deploy your workflows you can do it directly from your CLI.

To make this possible, run this in your CLI to install:

You can then run your collections by;

To run all the requests within a specific folder, use;

If you want to run your collection within a specific environment, use;

To dive deeper, you can check out Bruno CLI’s documentation.

Conclusion: Is Bruno just another API client?

To conclude, Bruno is a modern API Client that aims to rise against the monopoly of bloated and closed systems. It is a lightweight, easy-to-use API Client that does not require you to create an account or a constant internet connection to use it–unlike Postman & Insomnia.

Even though it’s still new, it disrupts the status quo set up by traditional API Clients by providing data sovereignty, resource efficiency, and straightforward usability. This positions it as more than just another API Client in the market.